Deploying and growing a set of dispersed storage units at and by non-information dispersal algorithm (IDA) width multiples

ABSTRACT

Methods and apparatus for use in a dispersed storage network (DSN) to deploy and grow a set of dispersed storage (DS) units for use in the DSN memory. In an example of operation, a DS client module assigns one or more additional DS units to a storage set to form a new storage set, where data is encoded in the DSN utilizing a dispersed storage error encoding function in accordance with an information dispersal algorithm (IDA) width. For each encoded data slice stored in the existing storage set, the DS client module utilizes a distributed agreement protocol function to select a storage unit of the new storage set for storage of the encoded data slice.

CROSS REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/248,752, filed30 Oct. 2015, entitled “MIGRATING DATA IN A DISPERSED STORAGE NETWORK,”which is hereby incorporated herein by reference in its entirety andmade part of the present U.S. Utility Patent Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks, and moreparticularly to cloud storage.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on a remote storagesystem. The remote storage system may include a RAID (redundant array ofindependent disks) system and/or a dispersed storage system that uses anerror correction scheme to encode data for storage.

In a RAID system, a RAID controller adds parity data to the originaldata before storing it across an array of disks. The parity data iscalculated from the original data such that the failure of a single disktypically will not result in the loss of the original data. While RAIDsystems can address certain memory device failures, these systems maysuffer from effectiveness, efficiency and security issues. For instance,as more disks are added to the array, the probability of a disk failurerises, which may increase maintenance costs. When a disk fails, forexample, it needs to be manually replaced before another disk(s) failsand the data stored in the RAID system is lost. To reduce the risk ofdata loss, data on a RAID device is often copied to one or more otherRAID devices. While this may reduce the possibility of data loss, italso raises security issues since multiple copies of data may beavailable, thereby increasing the chances of unauthorized access. Inaddition, co-location of some RAID devices may result in a risk of acomplete data loss in the event of a natural disaster, fire, powersurge/outage, etc.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) in accordance with the presentdisclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present disclosure;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an example of slice naminginformation for an encoded data slice (EDS) in accordance with thepresent disclosure;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present disclosure;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present disclosure;

FIG. 9 is a schematic block diagram of an example of a dispersed storagenetwork in accordance with the present disclosure;

FIG. 10A is a schematic block diagram of an embodiment of adecentralized agreement module in accordance with the present invention;

FIG. 10B is a flowchart illustrating an example of selecting theresource in accordance with the present invention;

FIG. 10C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 10D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIG. 11A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 11B is a flowchart illustrating an example of migrating data inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofdispersed storage (DS) computing devices or processing units 12-16, amanaging unit 18, an integrity processing unit 20, and a DSN memory 22.The components of the DSN 10 are coupled to a network 24, which mayinclude one or more wireless and/or wire lined communication systems;one or more non-public intranet systems and/or public internet systems;and/or one or more local area networks (LAN) and/or wide area networks(WAN).

The DSN memory 22 includes a plurality of dispersed storage units 36 (DSunits) that may be located at geographically different sites (e.g., onein Chicago, one in Milwaukee, etc.), at a common site, or a combinationthereof. For example, if the DSN memory 22 includes eight dispersedstorage units 36, each storage unit is located at a different site. Asanother example, if the DSN memory 22 includes eight storage units 36,all eight storage units are located at the same site. As yet anotherexample, if the DSN memory 22 includes eight storage units 36, a firstpair of storage units are at a first common site, a second pair ofstorage units are at a second common site, a third pair of storage unitsare at a third common site, and a fourth pair of storage units are at afourth common site. Note that a DSN memory 22 may include more or lessthan eight storage units 36.

Each of the DS computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, and network orcommunications interfaces 30-33 which can be part of or external tocomputing core 26. DS computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the dispersed storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data object 40) as subsequently describedwith reference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-16 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20. The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generateper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network operations can furtherinclude monitoring read, write and/or delete communications attempts,which attempts could be in the form of requests. Network administrationincludes monitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

To support data storage integrity verification within the DSN 10, theintegrity processing unit 20 (and/or other devices in the DSN 10 such asmanaging unit 18) may assess and perform rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. Retrieved encoded slices are assessed and checked for errorsdue to data corruption, outdated versioning, etc. If a slice includes anerror, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slicesthat are not received and/or not listed may be flagged as missingslices. Bad and/or missing slices may be subsequently rebuilt usingother retrieved encoded data slices that are deemed to be good slices inorder to produce rebuilt slices. A multi-stage decoding process may beemployed in certain circumstances to recover data even when the numberof valid encoded data slices of a set of encoded data slices is lessthan a relevant decode threshold number. The rebuilt slices may then bewritten to DSN memory 22. Note that the integrity processing unit 20 maybe a separate unit as shown, included in DSN memory 22, included in thecomputing device 16, managing unit 18, stored on a DS unit 36, and/ordistributed among multiple storage units 36.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber. In the illustrated example, the value X11=aD1+bD5+cD9,X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as at least part of a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

In order to recover a data segment from a decode threshold number ofencoded data slices, the computing device uses a decoding function asshown in FIG. 8. As shown, the decoding function is essentially aninverse of the encoding function of FIG. 4. The coded matrix includes adecode threshold number of rows (e.g., three in this example) and thedecoding matrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a diagram of an example of a dispersed storage network. Thedispersed storage network includes a DS (dispersed storage) clientmodule 34 (which may be in DS computing devices 12 and/or 16 of FIG. 1),a network 24, and a plurality of DS units 36-1 . . . 36-n (which may bestorage units 36 of FIG. 1 and which form at least a portion of DSmemory 22 of FIG. 1), a DSN managing unit (not shown—device 18 in FIG.1), and a DS integrity verification module 20. The DS client module 34includes an outbound DS processing section 81 and an inbound DSprocessing section 82. Each of the DS units 36-1 . . . 36-n includes acontroller 86, a processing module 84 including a communicationsinterface for communicating over network 24 (not shown), memory 88, a DT(distributed task) execution module 90, and a DS client module 34.

In an example of operation, the DS client module 34 receives data 92.The data 92 may be of any size and of any content, where, due to thesize (e.g., greater than a few Terabytes), the content (e.g., securedata, etc.), and/or concerns over security and loss of data, distributedstorage of the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DS client module 34, the outbound DS processing section 81receives the data 92. The outbound DS processing section 81 processesthe data 92 to produce slice groupings 96. As an example of suchprocessing, the outbound DS processing section 81 partitions the data 92into a plurality of data partitions. For each data partition, theoutbound DS processing section 81 dispersed storage (DS) error encodesthe data partition to produce encoded data slices and groups the encodeddata slices into a slice grouping 96.

The outbound DS processing section 81 then sends, via the network 24,the slice groupings 96 to the DS units 36-1 . . . 36-n of the DSN memory22 of FIG. 1. For example, the outbound DS processing section 81 sendsslice group to DS storage unit 36-1. As another example, the outbound DSprocessing section 81 sends slice group #n to DS unit #n.

In one example of operation, the DS client module 34 requests retrievalof stored data within the memory of the DS units 36. In this example,the task 94 is retrieve data stored in the DSN memory 22. Accordingly,and according to one embodiment, the outbound DS processing section 81converts the task 94 into a plurality of partial tasks 98 and sends thepartial tasks 98 to the respective DS storage units 36-1 . . . 36-n.

In response to the partial task 98 of retrieving stored data, a DSstorage unit 36 identifies the corresponding encoded data slices 99 andretrieves them. For example, DS unit #1 receives partial task #1 andretrieves, in response thereto, retrieved slices #1. The DS units 36send their respective retrieved slices 99 to the inbound DS processingsection 82 via the network 24.

The inbound DS processing section 82 converts the retrieved slices 99into data 92. For example, the inbound DS processing section 82de-groups the retrieved slices 99 to produce encoded slices per datapartition. The inbound DS processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DS processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 10A is a schematic block diagram of an embodiment of adecentralized agreement module 350 that includes a set of deterministicfunctions 340-1 . . . 340-N, a set of normalizing functions 342-1 . . .342-N, a set of scoring functions 344-1 . . . 344-N, and a rankingfunction 352. Each of the deterministic function, the normalizingfunction, the scoring function, and the ranking function 352, may beimplemented utilizing the processing module 84 of FIG. 9. Thedecentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module is implemented utilizing thedistributed storage (DS) client module 34 of FIG. 1.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations include one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a DS processing unit (computing device) 16of FIG. 1, a DS integrity processing unit 20 of FIG. 1, a DSN managingunit 18 of FIG. 1, a user device (computing device) 12 of FIG. 1, and auser device (computing device) 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result 341-1 . . . 341-N.The deterministic function includes at least one of a hashing function,a hash-based message authentication code function, a mask generatingfunction, a cyclic redundancy code function, hashing module of a numberof locations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 340-2 appends alocation ID 339-2 of a storage pool to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 341-2.

With a set of interim results 341-1 . . . 341-N, each normalizingfunction 342-1 . . . 342N performs a normalizing function on acorresponding interim result to produce a corresponding normalizedinterim result. The performing of the normalizing function includesdividing the interim result by a number of possible permutations of theoutput of the deterministic function to produce the normalized interimresult. For example, normalizing function 342-2 performs the normalizingfunction on the interim result 341-2 to produce a normalized interimresult 343-2.

With a set of normalized interim results 343-1 . . . 343-N, each scoringfunction performs a scoring function on a corresponding normalizedinterim result to produce a corresponding score. The performing of thescoring function includes dividing an associated location weight by anegative log of the normalized interim result. For example, scoringfunction 344-2 divides location weight 345-2 of the storage pool (e.g.,associated with location ID 339-2) by a negative log of the normalizedinterim result 343-2 to produce a score 346-2.

With a set of scores 346-1 . . . 346-N, the ranking function 352performs a ranking function on the set of scores 346-1 . . . 346-N togenerate the ranked scoring information 358. The ranking functionincludes rank ordering each score with other scores of the set of scores346-1 . . . 346-N, where a highest score is ranked first. As such, alocation associated with the highest score may be considered a highestpriority location for resource utilization (e.g., accessing, storing,retrieving, etc., the given asset of the request). Having generated theranked scoring information 358, the decentralized agreement module 350outputs the ranked scoring information 358 to the requesting entity.

FIG. 10B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 10C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage (DS)processing unit (computing device) 16 of FIG. 1, the network 24 of FIG.1, and the distributed storage network (DSN) module 22 of FIG. 1.Hereafter, the DSN module 22 may be interchangeably referred to as a DSNmemory. The DS processing unit 16 includes a decentralized agreementmodule 380 and the DS client module 34 of FIG. 1. The decentralizedagreement module 380 being implemented utilizing the decentralizedagreement module 350 of FIG. 10A. The DSN module 22 includes a pluralityof DS unit pools 400-1 . . . 400-N. Each DS unit pool includes one ormore sites 402-1 . . . 402-N. Each site includes one or more DS units404-1-1 . . . 404-1-N. Each DS unit may be associated with at least onepillar of N pillars associated with an information dispersal algorithm(IDA) (406-1 . . . 406-N), where a data segment is dispersed storageerror encoded using the IDA to produce one or more sets of encoded dataslices, and where each set includes N encoded data slices and likeencoded data slices (e.g., slice 3's) of two or more sets of encodeddata slices are included in a common pillar (e.g., pillar 406-3). Eachsite may not include every pillar and a given pillar may be implementedat more than one site. Each DS unit includes a plurality of memories(e.g. DS unit 404-1-1 includes memories 408-1-1-1 . . . 408-1-1-N. EachDS unit may be implemented utilizing the DS unit 36 of FIG. 1 and thememories 408 of DS units can be implemented utilizing memory 88 of DSunit 36 in FIG. 9. Hereafter, a DS unit may be referred tointerchangeably as a storage unit and a set of DS units may beinterchangeably referred to as a set of storage units and/or as astorage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DS unit pool for data access, utilize the selected DSunit pool for the data access, and issue a data access response 392based on the data access. The selecting of the resources includesutilizing a decentralized agreement function of the decentralizedagreement module 380, where a plurality of locations are ranked againsteach other. The selecting may include selecting one storage pool of theplurality of storage pools, selecting DS units at various sites of theplurality of sites, selecting a memory of the plurality of memories foreach DS unit, and selecting combinations of memories, DS units, sites,pillars, and storage pools.

In an example of operation, the DS client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theDS client module 34 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, a plurality of setsof slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DS client module 34 selects aplurality of resource levels (e.g., DS unit pool, site, DS unit, pillar,memory) associated with the DSN module 22. The determining may be basedon one or more of the data name, the requesting entity ID, apredetermination, a lookup, a DSN performance indicator, andinterpreting an error message. For example, the DS client module 34selects the DS unit pool as a first resource level and a set of memorydevices of a plurality of memory devices as a second resource levelbased on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality of resource levels, the DS client module34, for each resource level, issues a ranked scoring information request384 to the decentralized agreement module 380 utilizing the DSN addressas an asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DS client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DS client module 34 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the DS client module 34 identifies a DSunit pool associated with a highest score and identifies a set of memorydevices within DS units of the identified DS unit pool with a highestscore.

Having identified the one or more resources, the DS client module 34accesses the DSN module 22 based on the identified one or more resourcesassociated with each resource level. For example, the DS client module34 issues resource access requests 388 (e.g., write slice requests whenstoring data, read slice requests when recovering data) to theidentified DS unit pool, where the resource access requests 388 furtheridentify the identified set of memory devices. Having accessed the DSNmodule 22, the DS client module 34 receives resource access responses390 (e.g., write slice responses, read slice responses). The DS clientmodule 34 issues the data access response 392 based on the receivedresource access responses 390. For example, the DS client module 34decodes received encoded data slices to reproduce data and generates thedata access response 392 to include the reproduced data.

FIG. 10D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 410where a processing module (e.g., of a distributed storage (DS) clientmodule) receives a data access request from a requesting entity. Thedata access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 412 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 414 where the processing module selects aplurality of resource levels associated with the DSN memory. Theselecting may be based on one or more of a predetermination, a range ofweights associated with available resources, a resource performancelevel, and a resource performance requirement level. For each resourcelevel, the method continues at step 416 where the processing moduledetermines ranked scoring information. For example, the processingmodule issues a ranked scoring information request to a decentralizedagreement module based on the DSN address and receives correspondingranked scoring information for the resource level, where thedecentralized agreement module performs a decentralized agreementprotocol function on the DSN address using the associated resourceidentifiers and resource weights for the resource level to produce theranked scoring information for the resource level.

For each resource level, the method continues at step 418 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 420 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 422 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

In one example of operation, the DSN of FIG. 1 is grown to accommodateadditional DS units. Further explanations of this process of deployingand growing a set of ds units at and by non-IDA width multiples are setout below in conjunction with FIGS. 11A and 11B. When DS units aredeployed in a DSN memory with at least an IDA width number of DS unitsat a time, then maximum failure independence and accordingly, maximumreliability and availability are achieved. This set of DS units may beused to create or expand a storage pool for example. However, when fewerthan an IDA width number of DS units are deployed, it is necessary thatsome DS units will store more than one slice for the same data source(e.g. when storing 15 slices across 5 DS units, each DS unit might store3 slices each for the same data source). At some future time, it maybecome necessary to expand the DSN memory with more DS units. If the DSNmemory was initially deployed with fewer than IDA width number of DSunits then it may be desirable to use the additional DS units to moreevenly distribute slices across a larger number of DS units, therebyimproving reliability and availability. For example, two options existfor growing the initial deployment of 5 DS units when growing by anadditional 5 DS units. Option 1: Treat each set of 5 DS units (each set)independently, and in a 15-wide continue storing 3 slices each to eachDS unit and store all slices on either the first set of 5 DS units, orthe second set of 5 DS units. Option 2: Use the existing set of 5 DSunits, together with the new set of 5 DS units, to form a larger setcontaining 10 DS units, over which some no DS unit need to store morethan 2 slices of the same source. The second option is preferable from areliability and availability perspective.

To grow the system in this second way, the existing system expansion byreallocation via a Decentralized Agreement Protocol (DAP) can, accordingto one example, be used as follows:

1. Maintain the existing set of DS units as its own independent set in astorage pool;

2. Form a second set of DS units composed of the existing DS unitstogether with the new DS units;

3. Initiate a reallocation of slices between these two sets, e.g. bysetting the weight of the first set to “0” and the weight of the newlyformed composite set equal to the size of the total number of DS unitsin the composite set;

4. Migrate slices from the smaller set to the larger set, moving slicesto their new location in the new set within which each DS unit has asmaller fraction of the namespace; and

5. When the migration of all slices is complete, eliminate the originalset of DS units, leaving behind only the new composite set.

In this way a set of DS units can be grown by as little as one DS unitat a time. However, once the set is grown to a size equal to 2*IDAwidth, it may make sense to “break” the large set into two smaller sets,each of size IDA width (set's in the sense of independent locationswhich slices may be mapped to by a Decentralized Agreement Protocol).Once the set is broken in this way, only the second set is grown, whilethe previous sets (each containing IDA width DS units) remain unchangedin the pool and is not expanded in this manner. The motivation forbreaking off sets is it makes expanding the system by fewer than IDAwidth at a time more efficient. The fewer DS units in the set that isexpanded in this way, the less total data transfer is required.

FIG. 11A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storage(DS) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and atleast two storage sets 500-1 and 500-2. The DS processing unit(computing device) 16 includes the DS client module 34 of FIG. 1 and adecentralized agreement module. The decentralized agreement module maybe implemented utilizing the decentralized agreement module 350 of FIG.10A. Each storage set includes a set of storage units 36-n and may beexpanded to accommodate increasing a storage capacity level of thestorage set. For example, the storage set 500-1 initially includesstorage units 36-1 to 36-5 and is expanded to include storage units 36-6to 36-10 to form the storage set 500-2. Each storage unit may beimplemented utilizing the DS units 36 of FIG. 1. The DSN functions tomigrate data when the set of storage units is expanded.

In an example of operation of the migrating of data, the DS clientmodule 34 assigns one or more additional dispersed storage units to thestorage set 500-1 to form a new storage set 500-2, where data is encodedutilizing a dispersed storage error encoding function in accordance withan information dispersal algorithm (IDA) width to produce a plurality ofsets of encoded data slices that the DS processing unit 16 stores in thestorage set 500-1 and where each set of encoded data slices includes anIDA width number of encoded data slices. For example, the DS processingunit 16 stores three encoded data slices per storage unit of the storageunits 36-1 to 36-5 when the IDA width is 15. The assigning of the one ormore additional storage units includes one or more of determining anumber of additional storage units, identifying available storage units,and selecting from the dispersed storage units identified for assignmentby the middle storage units to produce the one or more additionalstorage units. The determining of the number of additional storage unitsto add may be based on one or more of estimated future storagerequirements, an existing storage utilization level, and apredetermination.

For each encoded data slice stored in the existing storage set 500-1,the DS client module 34 utilizes a distributed agreement protocolfunction to select a storage unit of the new storage set 500-2 forstorage of an encoded data slice. This function may be implementedutilizing any module and/or unit of a dispersed storage network (DSN)including the DS Managing Unit 18, the Integrity Processing Unit 20,and/or by one or more DS units 36-1 . . . 36-n shown in FIG. 1. Forexample, the DS client module 34 utilizes the decentralized agreementmodule to perform the distributed agreement protocol function on a slicename associated with encoded data slice utilizing updated weights foreach of the storage units of the existing storage set and newlyestablished weights for each of the additional storage units to producea score for each storage unit of the new storage set and identifies astorage unit associated with a highest score as the selected storageunit of the new storage set for storage of the encoded data slice.

Having selected the storage unit, the DS client module 34 facilitatesmigration of the encoded data slice from the existing storage set 500-1to the selected storage unit of the new storage set 500-2 when theencoded data slice is not presently stored in the selected storage unit.This could include migration to new DS units 36-6 to 36-10. For example,the DS client module 34 receives, via the network 24, encoded dataslices of storage set 500-1 (502) that includes encoded data slice, andsends, via the network 24, encoded data slices of storage set 500-2(504) that includes the encoded data slice for migration, to theselected storage unit of the new storage set 500-2 for storage.

FIG. 11B is a flowchart illustrating an example of migrating data. Themethod includes a step 600 where a processing module of one or moreprocessing modules of one or more computing devices (e.g., of adistributed storage (DS) client module) assigns one or more additionalstorage units to an existing storage set to form a new storage set of adispersed storage network (DSN). The assigning includes one or more ofdetermining a number of additional storage units (e.g., based on one ormore of a predetermination, estimated future storage requirement, andexisting storage utilization level), identifying available storageunits, and selecting from the identified available storage units basedon the number of additional storage units.

For each encoded data slice stored in existing storage set, the methodcontinues at the step 602 where the processing module utilizes adistributed agreement protocol function to select a storage unit of thenew storage set for storage of the encoded data slice. For example, theprocessing module performs the distributed agreement protocol functionon a slice name associated with encoded data slice utilizing updatedweights for the storage units of the existing storage set and newlyestablished weights for the additional storage units of the new storageset to produce a score for each storage unit of the storage set andidentifies a storage unit associated with a highest score of a pluralityof scores as the selected storage unit.

The method continues at the step 604 where the processing modulefacilitates migration of encoded data slice from the existing storageset to the selected storage unit of the storage set when the encodeddata slice is not presently stored within the selected storage unit. Forexample, the processing module retrieves encoded data slice from theexisting storage set and sends the encoded data slice to the selectedstorage unit for storage.

The methods described above in conjunction with the computing device andthe storage units can alternatively be performed by other modules of thedispersed storage network or by other devices. For example, anycombination of a first module, a second module, a third module, a fourthmodule, etc. of the computing device and the storage units may performthe method described above. In addition, at least one memory section(e.g., a first memory section, a second memory section, a third memorysection, a fourth memory section, a fifth memory section, a sixth memorysection, etc. of a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices and/or by thestorage units of the dispersed storage network (DSN), cause the one ormore computing devices and/or the storage units to perform any or all ofthe method steps described above.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent. Such relativitybetween items ranges from a difference of a few percent to magnitudedifferences. As may also be used herein, the term(s) “configured to”,“operably coupled to”, “coupled to”, and/or “coupling” includes directcoupling between items and/or indirect coupling between items via anintervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for an exampleof indirect coupling, the intervening item does not modify theinformation of a signal but may adjust its current level, voltage level,and/or power level. As may further be used herein, inferred coupling(i.e., where one element is coupled to another element by inference)includes direct and indirect coupling between two items in the samemanner as “coupled to”. As may even further be used herein, the term“configured to”, “operable to”, “coupled to”, or “operably coupled to”indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signalA has a greater magnitude than signal B, a favorable comparison may beachieved when the magnitude of signal A is greater than that of signal Bor when the magnitude of signal B is less than that of signal A. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from Figureto Figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information. A computer readable memory/storage medium,as used herein, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method of growing a dispersed storage network,the dispersed storage network including a first set of dispersed storageunits, wherein a first dispersed storage unit of the first set ofdispersed storage units stores a first encoded data slice and a secondencoded data slice and wherein the first encoded data slice and thesecond encoded data slice originate from a first data source, the methodcomprising: assigning one or more additional dispersed storage units tothe dispersed storage network including the first set of dispersedstorage units to form a second set of dispersed storage units the secondset of dispersed storage units including the first set of dispersedstorage units and the one or more additional dispersed storage units;reallocating the first encoded data slice from the first dispersedstorage unit to at least one of the one or more additional dispersedstorage units of the second set of dispersed storage units that does notpresently store the first encoded data slice; and facilitating migrationof the first encoded data slice from the first dispersed storage unit tothe at least one of the one or more additional dispersed storage unitsof the second set of dispersed storage units that does not presentlystore the first encoded data slice.
 2. The method of claim 1, whereinthe dispersed storage units in the first set of dispersed storage unitsare fewer than an information dispersal algorithm width number.
 3. Themethod of claim 1, wherein assigning one or more additional dispersedstorage units to the dispersed storage network comprises determining anumber of additional dispersed storage units.
 4. The method of claim 3,wherein assigning one or more additional dispersed storage units to thedispersed storage network comprises identifying the one or moreadditional dispersed storage units.
 5. The method of claim 4, furthercomprising selecting the one or more additional dispersed storage unitsidentified for assignment.
 6. The method of claim 3, wherein determiningthe number of additional dispersed storage units is based on one or moreof a predetermination, an estimated future storage requirements andexisting storage utilization levels.
 7. The method of claim 1, whereinassigning one or more additional dispersed storage units to thedispersed storage network uses a distributed agreement protocol.
 8. Themethod of claim 7, wherein the distributed agreement protocol updatesfirst weights for dispersed storage units of the first set of dispersedstorage units and establishes second weights for the one or moreadditional dispersed storage units.
 9. The method of claim 1, whereinfacilitating migration comprises sending the first encoded data slice toa dispersed storage computing device.
 10. A first dispersed storage unitof a first set of dispersed storage units for use in a dispersed storagenetwork, the first dispersed storage unit comprising: a communicationsinterface; a memory; and a processor; wherein the memory includes afirst encoded data slice and a second encoded data wherein the firstencoded data slice and the second encoded data slice originate from afirst data source and wherein the memory further includes instructionsfor causing the processor to: assign one or more additional dispersedstorage units to the dispersed storage network including the first setof dispersed storage units to form a second set of dispersed storageunits the second set of dispersed storage units including the first setof dispersed storage units and the one or more additional dispersedstorage units; reallocate the first encoded data slice from the firstdispersed storage unit to at least one of the one or more additionaldispersed storage units of the second set of dispersed storage unitsthat does not presently store the first encoded data slice; andfacilitate migration of the first encoded data slice from the firstdispersed storage unit to the at least one of the one or more additionaldispersed storage units of the second set of dispersed storage unitsthat does not presently store the first encoded data slice.
 11. Thefirst dispersed storage unit of claim 10, wherein the dispersed storageunits in the first set of dispersed storage units are fewer than aninformation dispersal algorithm width number.
 12. The first dispersedstorage unit of claim 10, wherein the memory further comprisesinstructions for causing the processor to determine a number ofadditional dispersed storage units.
 13. The first dispersed storage unitof claim 12, wherein the memory further comprises instructions forcausing the processor identify the one or more additional dispersedstorage units.
 14. The first dispersed storage unit of claim 13, whereinthe memory further comprises instructions for causing the processor toselect the one or more additional dispersed storage units forassignment.
 15. The first dispersed storage unit of claim 12, whereinthe instructions for causing the processor to determine a number ofadditional dispersed storage units uses one or more of apredetermination, estimated future storage requirements and existingstorage utilization levels.
 16. The first dispersed storage unit ofclaim 10, wherein the instructions for causing the processor to assignone or more additional dispersed storage units to the dispersed storagenetwork uses a distributed agreement protocol.
 17. The first dispersedstorage unit of claim 16, wherein the distributed agreement protocol isoperable to update first weights for dispersed storage units of thefirst set of dispersed storage units and operable to establish secondweights for the one or more additional dispersed storage units.
 18. Thefirst dispersed storage unit of claim 10, wherein the memory furthercomprises instructions for causing the processor to send the firstencoded data slice to a dispersed storage computing device.
 19. Adispersed storage network comprising: a first set of dispersed storageunits including a first dispersed storage unit; the first dispersedstorage unit including: a communications interface; a memory; and aprocessor; wherein the memory includes a first encoded data slice and asecond encoded data wherein the first encoded data slice and the secondencoded data slice originate from a first data source and wherein thememory further includes instructions for causing the processor to:assign one or more additional dispersed storage units to the dispersedstorage network including the first set of dispersed storage units toform a second set of dispersed storage units the second set of dispersedstorage units including the first set of dispersed storage units and theone or more additional dispersed storage units; reallocate the firstencoded data slice from the first dispersed storage unit to at least oneof the one or more additional dispersed storage units of the second setof dispersed storage units that does not presently store the firstencoded data slice; and facilitate migration of the first encoded dataslice from the first dispersed storage unit to the at least one of theone or more additional dispersed storage units of the second set ofdispersed storage units that does not presently store the first encodeddata slice.
 20. The dispersed storage network of claim 19, wherein thedispersed storage units in the first set of dispersed storage units arefewer than an information dispersal algorithm width number.